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A hybrid artificial intelligence control of a turbulent jet: Reynolds number effect and scaling

Published online by Cambridge University Press:  27 May 2022

A.K. Perumal Open the ORCID record for A.K. Perumal [Opens in a new window] ,
Z. Wu Open the ORCID record for Z. Wu [Opens in a new window] ,
D.W. Fan Open the ORCID record for D.W. Fan [Opens in a new window]  and
Y. Zhou
A.K. Perumal
Affiliation:
Center for Turbulence Control, Harbin Institute of Technology (Shenzhen), 518055 Shenzhen, PR China Jetacoustics and flow-control laboratory, Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
Z. Wu
Affiliation:
Center for Turbulence Control, Harbin Institute of Technology (Shenzhen), 518055 Shenzhen, PR China
D.W. Fan
Affiliation:
Center for Turbulence Control, Harbin Institute of Technology (Shenzhen), 518055 Shenzhen, PR China
Y. Zhou*
Affiliation:
Center for Turbulence Control, Harbin Institute of Technology (Shenzhen), 518055 Shenzhen, PR China
*
†Email address for correspondence: [email protected]
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Abstract

This work aims to investigate experimentally the effect of the Reynolds number Re, based on the nozzle diameter D, on jet mixing manipulation using an unsteady radial minijet. A novel artificial intelligence (AI) control system has been developed to manipulate the jet over Re = 5800–40 000. The system may optimize simultaneously the control law and a time-independent parameter, which dictate the actuation ON/OFF states and amplitude, respectively. The control parameters include the mass flow rate, excitation frequency and diameter ratios (Cm, fe/f0 and d/D) of the minijet to the main jet as well as the duty cycle (α) of minijet injection. Jet mixing is quantified using Ke and K0, where K is the decay rate of the jet centreline mean velocity, and subscripts e and 0 denote the manipulated and unforced jets, respectively. It has been found that the maximum Ke achievable does not vary with Re. Scaling analysis of the huge volume of experimental data obtained from the AI system reveals that the relationship Ke = g1 (Cm, fe/f0, α, d/D, Re, K0) may be reduced to Ke/K0 = g2 $(\zeta )$, where g1 and g2 are different functions and the scaling factor $\zeta = ({C_m}/\alpha ){(D/d)^{1 - n}}(1/Re){({f_e}/{f_{e,opt}})^m}$, m and 1 − n are the power indices, and subscript opt denotes the value at which Ke is maximum. The scaling law is discussed in detail, along with the physical meanings of the dimensionless parameters Ke/K0, ζ, $({C_m}/\alpha ){(D/d)^{1 - n}}(1/Re)$ and ${({f_e}/{f_{e,opt}})^m}$.

JFM classification

Flow Control: Mixing enhancement Flow Control: Control theory Wakes/Jets: Jets Mathematical Foundations: Machine learning
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 942 , 10 July 2022 , A47
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

‡

Joint first authors.

References

Arbey, H. & Williams, J.F. 1984 Active cancellation of pure tones in an excited jet. J. Fluid Mech. 149, 445–454.CrossRefGoogle Scholar
Beaudoin, J.F., Cadot, O., Aider, J.L. & Wesfreid, J.E. 2006 Drag reduction of a bluff body using adaptive control methods. Phys. Fluids 18 (8), 085107.CrossRefGoogle Scholar
Breidenthal, R.E., Tong, K.-O., Wong, G.S., Hamerquist, R.D. & Landry, P.B. 1985 Turbulent mixing in two-dimensional ducts with transverse jets. AIAA J. 21 (11), 1867–1869.Google Scholar
Brunton, S.L. & Noack, B.R. 2015 Closed-loop turbulence control: progress and challenges. Appl. Mech. Rev. 67 (5), 050801:01-48.CrossRefGoogle Scholar
Choi, H., Moin, P. & Kim, J. 1994 Active turbulence control for drag reduction in wall-bounded flows. J. Fluid Mech. 262, 75–110.CrossRefGoogle Scholar
Cox, T. & Cox, M. 2000 Multidimensional Scaling. Chapman & Hall.CrossRefGoogle Scholar
Crow, S.C. & Champagne, F.H. 1971 Orderly structure in jet turbulence. J. Fluid Mech. 48, 547–591.CrossRefGoogle Scholar
Duriez, T., Brunton, S.L. & Noack, B.R. 2016 Machine Learning Control — Taming Nonlinear Dynamics and Turbulence, Fluid Mechanics and Its Applications, vol. 116. Springer-Verlag.Google Scholar
Fan, D.W., Wu, Z., Yang, H., Li, J.D. & Zhou, Y. 2017 Modified extremum-seeking closed-loop system for jet mixing enhancement. AIAA J. 55 (11), 3891–3902.CrossRefGoogle Scholar
Fan, D.W., Zhou, Y. & Noack, B.R. 2020 Fast triple-parameter extremum seeking exemplified for jet control. Exp. Fluids 61 (7), 152.CrossRefGoogle Scholar
Freund, J.B. & Moin, P. 2000 Jet mixing enhancement by high-amplitude fluidic actuation. AIAA J. 38, 1863–1870.CrossRefGoogle Scholar
Gautier, N. & Aider, J.L. 2013 Control of the separated flow downstream of a backward-facing step using visual feedback. Proc. R. Soc. A-Math. Phys. Engng Sci. 469 (2160), 20130404.Google Scholar
Gautier, N., Aider, J.L., Durize, T., Noack, B.R., Segond, M. & Abel, M. 2015 Closed-loop separation control using machine learning. J. Fluid Mech. 770, 442–457.CrossRefGoogle Scholar
Henderson, B. 2010 Fifty years of fluidic injection for jet noise reduction. Intl J. Aeroacoust. 9, 91–122.CrossRefGoogle Scholar
Hermanson, J.C., Wahba, A. & Johari, H. 1998 Duty-cycle effects on penetration of fully modulated, turbulent jets in crossflow. AIAA J. 36, 1935–1937.CrossRefGoogle Scholar
Hussein, H., Capp, S. & George, W. 1994 Velocity measurements in a high-Reynolds-number, momentum-conserving, axisymmetric turbulent jet. J. Fluid Mech. 258, 31–75.CrossRefGoogle Scholar
Johari, H. 2006 Scaling of fully pulsed jets in crossflow. AIAA J. 44, 2719–2725.CrossRefGoogle Scholar
Johari, H., Pacheco-Tougas, M. & Hermanson, J.C. 1999 Penetration and mixing of fully modulated turbulent jets in crossflow. AIAA J. 37 (7), 842–850.CrossRefGoogle Scholar
Kaiser, E., Noack, B.R., Spohn, A., Cattafesta, L.N. & Morzyński, M. 2017 Cluster-based control of a separating flow over a smoothly contoured ramp. Theor. Comput. Fluid Dyn. 31 (5), 579–593.CrossRefGoogle Scholar
Kim, J. & Bewley, T. 2007 A linear systems approach to flow control. Annu. Rev. Fluid Mech. 39, 383–417.CrossRefGoogle Scholar
Knowles, K. & Saddington, A.J. 2006 A review of jet mixing enhancement for aircraft propulsion applications. Proc. IMechE Vol. 220 Part G: J. Aerosp. Engng 220 (2), 103–127.Google Scholar
Koza, J. R. 1990 Genetically breeding populations of computer programs to solve problems in artificial intelligence. In [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence, pp. 819-827. IEEE.Google Scholar
Li, R., Noack, B.R., Cordier, L., Boree, J. & Harambat, F. 2017 Drag reduction of a car model by linear genetic programming control. Exp. Fluids 58 (103), 1–20.CrossRefGoogle Scholar
Mi, J. & Nathan, G.J. 2010 Statistical properties of turbulent free jets issuing from nine differently-shaped nozzles. Flow Turbul. Combust. 84 (4), 583–606.CrossRefGoogle Scholar
Mi, J., Xu, M. & Zhou, T. 2013 Reynolds number influence on statistical behaviors of turbulence in a circular free jet. Phys. Fluids 25, 075101.CrossRefGoogle Scholar
New, T.H., Lim, T.T. & Luo, S.C. 2006 Effects of jet velocity profiles on a round jet in cross-flow. Exp. Fluids 40 (6), 859–875.CrossRefGoogle Scholar
Noack, B.R. 2018 Closed-loop turbulence control—from human to machine learning (and retour). In Proceedings of the 4th Symposium on Fluid Structure-Sound Interactions and Control (FSSIC), Tokyo, Japan (ed. Y. Zhou, M. Kimura, G. Peng, A.D. Lucey & L. Hung), pp. 1–10. Springer.CrossRefGoogle Scholar
Noack, B.R, Morzynski, M. & Tadmor, G. (Eds.) 2011 Reduced-Order Modelling for Flow Control, vol. 528. Springer Science & Business Media.CrossRefGoogle Scholar
Panchapakesan, N.R. & Lumley, L.J. 1993 Turbulence measurements in axisymmetric jets of air and helium. Part 1. Air jet. J. Fluid Mech. 246, 197.CrossRefGoogle Scholar
Parekh, D., Leonard, A. & Reynolds, W.C. 1988 Bifurcating jets at high Reynolds numbers. Department of Mechanical Engineering Thermosci. Div. Rep. TF35. Stanford University.Google Scholar
Parezanović, V., et al. 2015 Mixing layer manipulation experiment. Flow Turbul. Combust. 94 (1), 155–173.CrossRefGoogle Scholar
Pastoor, M., Henning, L., Noack, B.R., King, R. & Tadmor, G. 2008 Feedback shear layer control for bluff body drag reduction. J. Fluid Mech. 608, 161–196.CrossRefGoogle Scholar
Perumal, A.K. & Rathakrishnan, E. 2022 Design of fluidic injector for supersonic jet manipulation. AIAA J. 1–10.CrossRefGoogle Scholar
Perumal, A.K. & Zhou, Y. 2018 Parametric study and scaling of jet manipulation using an unsteady minijet. J. Fluid Mech. 848, 592–630.CrossRefGoogle Scholar
Perumal, A.K. & Zhou, Y. 2021 Axisymmetric jet manipulation using multiple unsteady minijets. Phys. Fluids 33 (6), 065124.CrossRefGoogle Scholar
Qiao, Z.X., Minelli, G., Noack, B.R., Krajnović, S. & Chernoray, V. 2021 Multi-frequency aerodynamic control of a yawed bluff body optimized with a genetic algorithm. J. Wind Engng Ind. Aerodyn. 212, 104600.CrossRefGoogle Scholar
Reynolds, W.C., Parekh, D.E., Juvet, P.J.D. & Lee, M.J.D. 2003 Bifurcating and blooming jets. Annu. Rev. Fluid Mech. 35 (1), 295–315.CrossRefGoogle Scholar
Ricou, F.P. & Spalding, D.B. 1961 Measurements of entrainment by axisymmetrical turbulent jets. J. Fluid Mech. 11, 21.CrossRefGoogle Scholar
Samimy, M., Kim, J.H., Kastner, J., Adamovich, I. & Utkin, Y. 2007 Active control of high-speed and high-Reynolds-number jets using plasma actuators. J. Fluid Mech. 578, 305–330.CrossRefGoogle Scholar
Steinfurth, B. & Weiss, J. 2020 Efficient vortex ring generation with non-parallel planar starting jets in crossflow. In AIAA Scitech 2020 Forum, p. 0814. American Institute of Aeronautics and Astronautics.CrossRefGoogle Scholar
Wickersham, P. 2007 Jet mixing enhancement by high amplitude pulse-fluidic actuator. PhD thesis, Georgia Institute of Technology.Google Scholar
Wu, Z., Fan, D., Zhou, Y., Li, R. & Noack, B.R. 2018 a Jet mixing optimization using machine learning control. Exp. Fluids 59 (8), 131.CrossRefGoogle Scholar
Wu, Z., Wong, C.W. & Zhou, Y. 2018 b Dual-input/single-output extremum-seeking system for jet control. AIAA J. 56 (4), 1463–1471.CrossRefGoogle Scholar
Wu, Z., Zhou, Y., Cao, H.L. & Li, W.L. 2016 Closed-loop enhancement of jet mixing with extremum-seeking and physics-based strategies. Exp. Fluids 57 (6), 107.CrossRefGoogle Scholar
Yang, H. & Zhou, Y. 2016 Axisymmetric jet manipulated using two unsteady minijets. J. Fluid Mech. 808, 362–396.CrossRefGoogle Scholar
Yang, H., Zhou, Y., So, R.M.C. & Liu, Y. 2016 Turbulent jet manipulation using two unsteady azimuthally separated radial minijets. Proc. R. Soc. A: Math., Phys. Eng. Sci. 472 (2191), 20160417.CrossRefGoogle ScholarPubMed
Yu, J., Fan, D., Noack, B. & Zhou, Y. 2021 Genetic-algorithm-based artificial intelligence control of a turbulent boundary layer. Acta Mechanica Sin. 37, 1739–1747.CrossRefGoogle Scholar
Zhang, M.M., Cheng, L. & Zhou, Y. 2004 Closed-loop-controlled vortex shedding and vibration of a flexibly supported square cylinder under different schemes. Phys. Fluids 16 (5), 1439–1448.CrossRefGoogle Scholar
Zhou, Y., Du, C., Mi, J. & Wang, X. 2012 Turbulent round jet control using two steady minijets. AIAA J. 50, 736–740.CrossRefGoogle Scholar
Zhou, Y., Fan, D., Zhang, B., Li, R. & Noack, B.R. 2020 Artificial intelligence control of a turbulent jet. J. Fluid Mech. 897, A27.CrossRefGoogle Scholar